The present invention relates generally to radiation therapy equipment for the treatment of tumors, and more particularly to methods for reconstructing incomplete patient data for radiation therapy and treatment verification.
Medical equipment for radiation therapy treats tumorous tissue with high energy radiation. The amount of radiation and its placement must be accurately controlled to ensure both that the tumor receives sufficient radiation to be destroyed, and that the damage to the surrounding and adjacent non-tumorous tissue is minimized.
External source radiation therapy uses a radiation source that is external to the patient to treat internal tumors. The external source is normally collimated to direct a beam only to the tumorous site. Typically, the tumor will be treated from multiple angles with the intensity and shape of the beam adjusted appropriately. The source of high energy radiation may be x-rays or electrons from a linear accelerator in the range of 2-25 MeV, or gamma rays from a highly focused radioisotope such as Co60 source having an energy of 1.25 MeV.
One form of external radiation therapy uses the precision of a computed tomography (CT) scanner to irradiate cancerous tissue in addition to acquiring CT images immediately before, immediately after, and/or during radiation treatment delivery. It is particularly useful to have online CT imaging capability integrated into a radiotherapy delivery system since it helps identify changes in a patient's position and anatomy between the time of imaging and treatment. However, many current patient imaging systems, especially ones that are integrated into radiotherapy treatment systems suffer from a limited field-of-view (LFOV) in that collected imaging data does not encompass the patient's complete cross-section. This LFOV can cause visibility problems with the images, images with artifacts, images with distorted values, and affect applications that use these images, including dose calculations, delivery verification, deformable patient registration, deformable dose registration, contouring (automatic, manual, or template-based).
Intensity modulated radiation therapy uses intensity modulated radiation beams that enter the patient's body at a greater number of angles and positions than conventional therapies, thereby lessening the amount of radiation that healthy tissues are subjected to and concentrating the radiation where it is needed most, at the cancer site(s). Essentially, the radiation field is “sculpted” to match the shape of the cancerous tissue and to keep the dose of radiation to healthy tissue near the cancer low. This type of radiotherapy greatly benefits from visualization of a patient's internal anatomy and accurate calculation of the delivered radiation dose. A radiation treatment plan may be based on a CT image of the patient. As is known in the art, a CT image is produced by a mathematical reconstruction of many projection images obtained at different angles about the patient. In a typical CT image, the projections are one-dimensional line profiles indicating the attenuation of the beam by a “slice” of the patient. The actual CT data is held in sinogram space as a matrix wherein each row represents a gantry position, a gantry angle, a ray angle or the like (a first sinogram dimension); each column represents a detector number, a detector distance, a detector angle, a ray position, or the like (a second sinogram dimension). A third sinogram dimension is commonly used with multi-row or volumetric detectors, representing each detector row. The matrix of data obtained in a CT image can be displayed as a sinogram 10 as shown in FIG. 1, or reconstructed into a two-dimensional image 12, as shown in FIG. 2.
In some radiotherapy systems, a physician views the cancerous areas on a CT image and determines the beam angles and intensities (identified with respect to the tumor image) which will be used to treat the tumor. In an automated system, such as that disclosed in U.S. Pat. No. 5,661,773, the disclosure of which is hereby incorporated by reference, a computer program selects the beam angles and intensities after the physician identifies the tumorous region and upper and lower dose limits for the treatment.
More specifically, planning CT images are used to create a three-dimensional (3-D) treatment plan of a region of interest. This region of interest is broken down into units called voxels, which are defined as volumetric pixels. Each voxel is then assigned a particular radiation dose depending on what type of tissue or other matter it contains, e.g. cancerous tissue, healthy tissue, air, water, etc.
Normally, the planning CT image of a patient is acquired substantially before the radiation treatment to allow time for the treatment plan to be prepared. However, the position of organs or other tissue to be treated can change from day-to-day because of a variety of factors. Further, patients move during treatment because of breathing, muscle twitching, or the like, and many patients are larger than the field-of-view (FOV) of the online CT imaging system. Uncertainty in the positioning of the patient with respect to the planning CT image can undermine the conformality of the radiation delivery.
Thus, it is highly preferable to verify the treatment plan based on data obtained just prior to the time of treatment. This verification process can be done by techniques that compare the planning image to an image of the patient at the time of treatment. Acquisition of an online tomographic image for the latter provides the benefits of 3-D tomographic imaging without requiring that the patient move between the imaging and treatment steps.
Unfortunately, the imaging data sets obtained on the day of treatment to be used for preparing the patient model are often incomplete or limited. These limitations may be caused by limited FOVs set by the field size of the multi-leaf collimator (MLC) attached to the linear accelerator and the detector size of the radiotherapy system. The limitations may also be caused by patients that are too large to fit within the FOV of the CT imaging system associated with the radiotherapy equipment applying the radiation dose, yielding a LFOV image as shown in FIG. 3, which shows only a portion of the image shown in FIG. 2. The FOV or image data sets may also be intentionally limited by modulated treatment data or region-of-interest tomography (ROIT) involving reconstruction of treatment data, intentionally only delivered to a specific region(s). For example, in FIG. 3, not only is there a LFOV, but the data around the edges contains significant artifacts so that the image has an irregular border and internal values that are distorted.
As mentioned above, the LFOV of radiotherapy images creates problems of impaired visibility and degraded dose calculations. The most common reasons for impaired visibility are the limited field size of the MLC attached to the linear accelerator and the limited detector size. These limitations prevent the CT imaging system from collecting complete FOV data for all sizes of patients at all sites. The problem of degraded dose calculations is caused by distorted electron densities and the loss of peripheral information for attenuation and scatter from the LFOV images. This distortion of image values and loss of peripheral information can likewise affect other applications that utilize these images.
To resolve the problem of limited imaging data sets in which only a portion of an image is obtained, several scans of the patient may be made at various detector or patient positions, and then combined into a complete set. This has been done by adding together sinogram data, but requires that the imaging apparatus or patient position can be reliably modified accordingly. This is often not possible. Further, the problem of artifacts is still present due to the significant degree of mismatch between such data sets, while the additional handling of the patient is more costly, time intensive and can be difficult for frail patients. Moreover, patients receiving multiple scans receive higher doses of radiation than with a single scan.
Reconstruction of incomplete imaging data sets using available techniques results in images that do not show the complete extent of the patient's body, can have artifacts and incorrect voxel values, and thus, limit the extent to which the images can be used for applications including delivery verification, dose reconstruction, patient set-up, contouring, deformable patient registration and deformable dose registration. Accordingly, a need exists for methods that can solve problems caused by limited imaging data sets.